通过人工智能 (AI) 的机器学习 (ML) 可以帮助临床医生和口腔病理学家推进潜在恶性病变、口腔癌、牙周病、唾液腺疾病、口腔感染、免疫介导疾病等领域的诊断问题。人工智能可以检测人眼之外的微观特征，并为关键的诊断病例​​提供解决方案。本研究的目的是开发一种包含所有所需喂养数据的软件，作为基于人工智能的程序来诊断口腔疾病。所以我们的研究问题是：我们能否开发一种基于临床和组织病理学数据输入的计算机辅助软件来准确诊断口腔疾病？研究样本包括口腔疾病的临床图像、患者症状、放射线图像、组织病理学图像和文本。本研究的兴趣（癌前病变、口腔癌、唾液腺肿瘤、免疫介导的口腔粘膜病变、口腔反应性病变） 纳入本研究的口腔疾病总数为从口腔颌面病理科档案中检索的 28 种疾病。总共11,200个文本和3000个图像（2800个图像用作程序的训练数据，100个图像用作程序的测试数据，100个案例用于计算准确性、灵敏度

Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases.The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs?The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity).The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree).The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.© 2024. The Author(s).